{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b18822e4",
   "metadata": {},
   "source": [
    "### 比较两个信号的频率成分\n",
    "#### 频谱相干性有助于识别频域中信号之间的相似性。大数值表示信号共有的频率分量。\n",
    "#### 将两个声音信号加载到工作区中。以 1 kHz 的频率对其进行采样。使用 periodogram 计算其功率频谱，并以彼此相邻的方式对其绘图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce1785f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.io import loadmat\n",
    "import numpy as np\n",
    "from scipy import signal\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.signal import find_peaks\n",
    "\n",
    "mat = loadmat(\"relatedsig.mat\")\n",
    "Fs = mat[\"FsSig\"]\n",
    "sig1 = mat[\"sig1\"]\n",
    "sig2 = mat[\"sig2\"]\n",
    "#npy_lsp = np.matrix.transpose(npy_lsp)\n",
    "f1, P1 = signal.periodogram(sig1, Fs)\n",
    "f2, P2 = signal.periodogram(sig2, Fs)\n",
    "\n",
    "\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.set_title('Power spectrum')\n",
    "ax1.plot(f1.flat, P1.flat)\n",
    "ax1.set_ylabel('P1')\n",
    "ax1.set_xlabel('Frequency(Hz)')\n",
    "\n",
    "fig, ax2 = plt.subplots()\n",
    "ax2.set_title('Power spectrum')\n",
    "ax2.plot(f2.flat, P2.flat)\n",
    "ax2.set_ylabel('P2', color='r')\n",
    "ax2.set_xlabel('Frequency(Hz)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "7bcba2c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 36.  95. 166. 264. 409. 490.]\n",
      "[ 35. 165. 350. 416.]\n"
     ]
    }
   ],
   "source": [
    "# peaks,_,_ = signal.find_peaks(P1.flat,height=0.05,distance=1)\n",
    "peakind = signal.find_peaks_cwt(P1.flat, np.arange(1,70))\n",
    "P1peakFreqs = f1.flat[peakind]\n",
    "print(P1peakFreqs)\n",
    "peakind = signal.find_peaks_cwt(P2.flat, np.arange(1,90))\n",
    "P2peakFreqs = f2.flat[peakind]\n",
    "print(P2peakFreqs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "f53edd6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,Cxy = signal.coherence(sig1,sig2,fs=Fs)\n",
    "thresh = 0.75\n",
    "pks,locs =signal.find_peaks(Cxy.flat,threshold=0.75)\n",
    "MatchingFreqs = f1.flat[pks]\n",
    "fig, ax3 = plt.subplots()\n",
    "ax3.set_title('Coherence Estimate')\n",
    "ax3.plot(f.flat, Cxy.flat)\n",
    "ax3.set_xlabel('Frequency(Hz)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "080d00c0",
   "metadata": {},
   "source": [
    "#### 您得到的值和以前一样。您可以找到两个信号共有的频率成分，而无需分别研究这两个信号。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32f7d421",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
